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US20040015507A1 - System and method for analytically modeling data organized according to related attributes - Google Patents

System and method for analytically modeling data organized according to related attributes Download PDF

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US20040015507A1
US20040015507A1 US10199913 US19991302A US2004015507A1 US 20040015507 A1 US20040015507 A1 US 20040015507A1 US 10199913 US10199913 US 10199913 US 19991302 A US19991302 A US 19991302A US 2004015507 A1 US2004015507 A1 US 2004015507A1
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table
data
according
attribute
customer
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US7287022B2 (en )
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Amir Netz
Cristian Petculescu
Mosha Pasumansky
Richard Tkachuk
Alexander Berger
Paul Sanders
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Microsoft Technology Licensing LLC
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Microsoft Corp
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    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor ; File system structures therefor
    • G06F17/30286Information retrieval; Database structures therefor ; File system structures therefor in structured data stores
    • G06F17/30587Details of specialised database models
    • G06F17/30592Multi-dimensional databases and data warehouses, e.g. MOLAP, ROLAP
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10STECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S707/00Data processing: database and file management or data structures
    • Y10S707/953Organization of data
    • Y10S707/954Relational
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10STECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S707/00Data processing: database and file management or data structures
    • Y10S707/953Organization of data
    • Y10S707/957Multidimensional
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10STECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S707/00Data processing: database and file management or data structures
    • Y10S707/953Organization of data
    • Y10S707/958Data cubes
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10STECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S707/00Data processing: database and file management or data structures
    • Y10S707/99931Database or file accessing
    • Y10S707/99933Query processing, i.e. searching
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10TECHNICAL SUBJECTS COVERED BY FORMER USPC
    • Y10STECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y10S707/00Data processing: database and file management or data structures
    • Y10S707/99941Database schema or data structure
    • Y10S707/99944Object-oriented database structure

Abstract

A system and method for analytically modeling data with related attributes is disclosed. A single dimension is used to provide data according to each of the related attributes, and, thus, may be said to play the role of each related attribute depending on a received query. The measure of the analytical data model is tied to the dimension according to both data attributes to allow the measure to be analyzed by the dimension according to both attributes.

Description

    BACKGROUND OF THE INVENTION
  • [0001]
    1. Field of the Invention
  • [0002]
    The present invention relates to systems and methods for analytically modeling data organized and stored in a relational database, and, more particularly, to analytically modeling data organized according to related attributes.
  • [0003]
    2. Description of the Prior Art
  • [0004]
    Online analytical processing (OLAP) is a key part of many data warehouse and business analysis systems. OLAP services provide for fast analysis of multidimensional information. For this purpose, OLAP services provide for multidimensional access and navigation of data in an intuitive and natural way, providing a global view of data that can be drilled down into particular data of interest. Speed and response time are important attributes of OLAP services that allow users to browse and analyze data online in an efficient manner. Further, OLAP services typically provide analytical tools to rank, aggregate, and calculate lead and lag indicators for the data under analysis.
  • [0005]
    In this context, an OLAP cube may be modeled according to a user's perception of the data. The cube may have multiple dimensions, each dimension modeled according to attributes of the data. Typically, there is a hierarchy associated with each dimension. For example, a time dimension can include years subdivided into months subdivided into weeks subdivided into days, while a geography dimension can include countries subdivided into states subdivided into cities. Dimension members act as indices for identifying a particular cell or range of cells within the cube.
  • [0006]
    OLAP services are often used to analytically model data that is stored in a relational database such as, for example, an Online Transactional Processing (OLTP) database. Data stored in a relational database may be organized according to multiple tables with each table having data corresponding to a particular data type. A table corresponding to a particular data type may be organized according to columns corresponding to data attributes. For example, data corresponding to the type “Sales” may be organized in a “Sales” table with columns “Ship-to Customer ID”, “Bill-to Customer ID”, and “Sale Quantity”. Furthermore, data corresponding to the type “Customer” may be organized in a “Customer” table with columns “Customer ID”, “Name”, “City”, and “State”.
  • [0007]
    The “Ship-to Customer ID” and “Bill-to Customer ID” attributes of the “Sales” table are related attributes because they both cross-reference the “Customer ID” attribute of the “Customer” table. For each ship-to customer, data corresponding to the customer's “Name”, “City”, and “State” is stored in the “Customer” table on the row having the ship-to customer's “Customer ID”. Likewise, for each bill-to customer, data corresponding to the customer's “Name”, “City”, and “State” is stored in the “Customer” table on the row having the bill-to customer's “Customer ID”.
  • [0008]
    One issue that arises with regard to analytically modeling data from a relational database is how to best take into consideration data with such related attributes. In existing methods for analytically modeling data with related attributes, a plurality of dimensions each provides data to one of the related attributes. For example, an OLAP cube may be modeled according to data stored in the “Sales” and “Customer” tables of a relational database. The cube may have a first dimension modeled according to the “Customer” type and providing data according to the “Ship-to Customer” attribute and a second dimension modeled according to the “Customer” type and providing data “Bill-to Customer” attribute.
  • [0009]
    Modeling two dimensions that each provide data to one of the related attributes is a complex and time-consuming process because, for each dimension, data must be retrieved from multiple tables. The complexity and time required to model the cube would be greatly reduced if, rather than having two dimensions that each provide data to one of the related attributes, the cube has a single dimension that provides data to both related attributes. Thus, there is a need in the art for a system and method for analytically modeling data with related attributes, the system and method having a single dimension providing data to a plurality of related attributes.
  • SUMMARY OF THE INVENTION
  • [0010]
    Accordingly, in the present invention, a system and method for analytically modeling data with related attributes is disclosed. In a relational database, a first table organizes a first type according to a first attribute and a second attribute, and a second table organizes a second type according to a third attribute. The first attribute of the first table is related to the third attribute of the second table such that the first table may be cross-referenced to the second table thereby. The second attribute of the first table is related to the third attribute of the second table such that the first table may be cross-referenced to the second table thereby.
  • [0011]
    The data stored in the relational database is analytically modeled. A measure is modeled according to the first type of the first table. A dimension is modeled according to the second type of the second table. The measure is tied to the dimension according to the first attribute of the first table and the third attribute of the second table to allow the measure to be analyzed by the dimension according to the first attribute. The measure is also tied to the dimension according to the second attribute of the first table and the third attribute of the second table to allow the measure to be analyzed by the dimension according to the second attribute. Thus, the dimension provides data according to both the first attribute of the first table and the second attribute of the first table.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • [0012]
    The illustrative embodiments will be better understood after reading the following detailed description with reference to the appended drawings, in which:
  • [0013]
    [0013]FIG. 1 is a block diagram representing a general purpose computer system in which aspects of the present invention and/or portions thereof may be incorporated;
  • [0014]
    [0014]FIG. 2 is a sample relational database table corresponding to “Sales” data;
  • [0015]
    [0015]FIG. 3 is a sample relational database table corresponding to “Customer” data;
  • [0016]
    [0016]FIG. 4 is a prior art analytical data cube derived from the tables of FIGS. 2 and 3;
  • [0017]
    [0017]FIG. 5 is an analytical data cube derived from the tables of FIGS. 2 and 3 in accordance with one embodiment of the present invention; and
  • [0018]
    [0018]FIG. 6 is a hierarchical data tree showing data organized in a dimension according to a plurality of gradations.
  • DETAILED DESCRIPTION
  • [0019]
    A system and method for analytically modeling data with related attributes is disclosed below with reference to the aforementioned drawings. Those skilled in the art will readily appreciate that the description given herein with respect to those drawings is for explanatory purposes only and is not intended in any way to limit the scope of the invention to the specific embodiments shown. Throughout the description, like reference numerals are employed to refer to like elements in the respective figures.
  • [0020]
    Computer Environment
  • [0021]
    [0021]FIG. 1 and the following discussion are intended to provide a brief general description of a suitable computing environment in which the present invention and/or portions thereof may be implemented. Although not required, the invention is described in the general context of computer-executable instructions, such as program modules, being executed by a computer, such as a client workstation or a server. Generally, program modules include routines, programs, objects, components, data structures and the like that perform particular tasks or implement particular abstract data types. Moreover, it should be appreciated that the invention and/or portions thereof may be practiced with other computer system configurations, including hand-held devices, multi-processor systems, microprocessor-based or programmable consumer electronics, network PCs, minicomputers, mainframe computers and the like. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.
  • [0022]
    As shown in FIG. 1, an exemplary general purpose computing system includes a conventional personal computer 120 or the like, including a processing unit 121, a system memory 122, and a system bus 123 that couples various system components including the system memory to the processing unit 121. The system bus 123 may be any of several types of bus structures including a memory bus or memory controller, a peripheral bus, and a local bus using any of a variety of bus architectures. The system memory includes read-only memory (ROM) 124 and random access memory (RAM) 125. A basic input/output system 126 (BIOS), containing the basic routines that help to transfer information between elements within the personal computer 120, such as during start-up, is stored in ROM 124.
  • [0023]
    The personal computer 120 may further include a hard disk drive 127 for reading from and writing to a hard disk (not shown), a magnetic disk drive 128 for reading from or writing to a removable magnetic disk 129, and an optical disk drive 130 for reading from or writing to a removable optical disk 131 such as a CD-ROM or other optical media. The hard disk drive 127, magnetic disk drive 128, and optical disk drive 130 are connected to the system bus 123 by a hard disk drive interface 132, a magnetic disk drive interface 133, and an optical drive interface 134, respectively. The drives and their associated computer-readable media provide non-volatile storage of computer readable instructions, data structures, program modules and other data for the personal computer 120.
  • [0024]
    Although the exemplary environment described herein employs a hard disk, a removable magnetic disk 129, and a removable optical disk 131, it should be appreciated that other types of computer readable media which can store data that is accessible by a computer may also be used in the exemplary operating environment. Such other types of media include a magnetic cassette, a flash memory card, a digital video disk, a Bernoulli cartridge, a random access memory (RAM), a read-only memory (ROM), and the like.
  • [0025]
    A number of program modules may be stored on the hard disk, magnetic disk 129, optical disk 131, ROM 124 or RAM 125, including an operating system 135, one or more application programs 136, other program modules 137 and program data 138. A user may enter commands and information into the personal computer 120 through input devices such as a keyboard 140 and pointing device 142. Other input devices (not shown) may include a microphone, joystick, game pad, satellite disk, scanner, or the like. These and other input devices are often connected to the processing unit 121 through a serial port interface 146 that is coupled to the system bus, but may be connected by other interfaces, such as a parallel port, game port, or universal serial bus (USB). A monitor 147 or other type of display device is also connected to the system bus 123 via an interface, such as a video adapter 148. In addition to the monitor 147, a personal computer typically includes other peripheral output devices (not shown), such as speakers and printers. The exemplary system of FIG. 1 also includes a host adapter 155, a Small Computer System Interface (SCSI) bus 156, and an external storage device 162 connected to the SCSI bus 156.
  • [0026]
    The personal computer 120 may operate in a networked environment using logical connections to one or more remote computers, such as a remote computer 149. The remote computer 149 may be another personal computer, a server, a router, a network PC, a peer device or other common network node, and typically includes many or all of the elements described above relative to the personal computer 120, although only a memory storage device 150 has been illustrated in FIG. 1. The logical connections depicted in FIG. 1 include a local area network (LAN) 151 and a wide area network (WAN) 152. Such networking environments are commonplace in offices, enterprise-wide computer networks, intranets, and the Internet.
  • [0027]
    When used in a LAN networking environment, the personal computer 120 is connected to the LAN 151 through a network interface or adapter 153. When used in a WAN networking environment, the personal computer 120 typically includes a modem 154 or other means for establishing communications over the wide area network 152, such as the Internet. The modem 154, which may be internal or external, is connected to the system bus 123 via the serial port interface 146. In a networked environment, program modules depicted relative to the personal computer 120, or portions thereof, may be stored in the remote memory storage device. It will be appreciated that the network connections shown are exemplary and other means of establishing a communications link between the computers may be used.
  • [0028]
    System and Method of the Present Invention
  • [0029]
    An analytical data service such as, for example, On-Line Analytical Processing (OLAP) may be employed to model data stored in a relational database such as, for example, an On-Line Transactional Processing (OLTP) database. As set forth previously, data stored in a relational database may be organized according to multiple tables, with each table having data corresponding to a particular data type. A table corresponding to a particular data type may be organized according to columns corresponding to data attributes. One such table is shown in FIG. 2, with data corresponding to the type “Sales” organized in a “Sales” table 200 with columns “Ship-to Customer ID” 210, “Bill-to Customer ID” 212, and “Sale Quantity” 214 and rows corresponding to individual sales entries. Another such table is shown in FIG. 3, with data corresponding to the type “Customer” organized in a “Customer” table 300 with columns “Customer ID” 310, “Name” 312, “City” 314, and “State” 316 and rows corresponding to individual customer entries.
  • [0030]
    The attributes “Ship-to Customer ID” 210 and “Bill-to Customer ID” 212 from “Sales” table 200 are related attributes because they both cross-reference the “Customer ID” attribute 310 from “Customer” table 300. That is, each ship-to customer in “Sales” table 200 is referenced according to a “Customer ID” 310 present in the “Customer” table 300 and data corresponding to a ship-to customer's name and city as stored in “Customer” table 300 on the row having the corresponding ship-to customer ID. Likewise, each bill-to customer in “Sales” table 200 is referenced according to a “Customer ID” 310 present in the “Customer” table 300 and data corresponding to a bill-to customer's name and city as stored in “Customer” table 300 on the row having the corresponding bill-to customer ID.
  • [0031]
    Referring now to FIG. 4, a prior art OLAP model of the data from “Sales” table 200 and “Customer” table 300 is shown as organized into a data cube 400. Cube 400 has a measure “Sales Quantity” 405 modeled according to the “Sales” type. Cube 400 has a first dimension 410 modeled according to the “Customer” type and providing data according to “Ship-to Customer” attribute 210. Cube 400 also has a second dimension 412 modeled according to the same “Customer” type and providing data according to “Bill-to Customer” attribute 212. As should be appreciated, other dimensions (not shown) may also be present based on other OLTP tables (not shown).
  • [0032]
    In cube 400, first dimension 410 and second dimension 412 each provide data to one of the related attributes “Ship-to Customer” 210 and “Bill-to Customer” 212, respectively. Again, modeling a cube 400 with two dimensions 410 and 412 that each provides data to one of two related attributes 210 and 212 is a complex and time-consuming process because, for each dimension, data must be retrieved from multiple tables 200 and 300. The complexity and time required to model the cube 400 would be greatly reduced if, rather than having two dimensions 410 and 412 that each provide data to one of the related attributes 210 and 212, the cube 400 had a single dimension that provided data to both related attributes 210 and 212. Thus, the system and method of the present invention models a cube with a single dimension providing data according to both related attributes 210 and 212.
  • [0033]
    In particular and referring now to FIG. 5, in one embodiment of the present invention, analytical data cube 500 is an OLAP model of the data from “Sales” table 200 and “Customer” table 300. As seen, cube 500 has a measure “Sales Quantity” 505 modeled according to the “Sales” type. Cube 500 also has a single dimension 510 modeled according to the “Customer” type of the “customer” table 300 of FIG. 3, where such dimension 510 provides data according to both “Ship-to Customer” attribute 210 and “Bill-to Customer” attribute 212 of “Sales” table 200. Unlike prior art data cube 400 of FIG. 4 that has two dimensions 410-412, each modeled according to the “Customer” type, data cube 500 of FIG. 5 has only the single dimension 510 modeled according to the “Customer” type and providing data for both the “Ship-to Customer” attribute 210 and the “Bill-to Customer” attribute 212, in effect playing the role of each attribute 210 and 212 according to a particular query. By eliminating the duplication of the second dimension, the single “role playing” dimension makes the cube easier to define and reduces both the time required to build the cube and the storage size required for the data in the cube.
  • [0034]
    Dimension 510 may have a dimension hierarchy represented in a grossly simplified fashion by data tree 600 as shown in FIG. 6. Nodes 610 and 611 in the top row are “State” nodes corresponding to “State” attribute 316 in FIG. 3. Nodes 620-624 in the second row are “City” nodes corresponding to “City” attribute 314 in FIG. 3. Nodes 630-637 in the third row are “Name” nodes corresponding to “Name” attribute 312 in FIG. 3. Nodes 640-649 in the fourth row are “Customer ID” nodes corresponding to “Customer ID” attribute 310 in FIG. 3. As should be appreciated, one advantage of a hierarchically organized analytical data model is that such a model allows data to be aggregated in response to a query. In particular, data aggregated according to the dimension hierarchy of FIG. 6 may be aggregated based on any of the levels in the hierarchy. For example, if a query requires a sales quantity for a specific city, then data may be aggregated by “City” attribute 314 and the second row of data tree 600.
  • [0035]
    Using cube 500 in response to a query should be apparent to the relevant public. Accordingly, no particular example is provided. Generally, based on whether a particular query requests data according to a bill-to customer or a ship-to customer, the dimension acts to play the role of each bill-to customer or each ship-to customer, respectively.
  • [0036]
    The programming necessary to effectuate the processes performed in connection with the present invention is relatively straight-forward and should be apparent to the relevant programming public. Accordingly, such programming is not attached hereto. Any particular programming, then, may be employed to effectuate the present invention without departing from the spirit and scope thereof.
  • [0037]
    While the invention has been described and illustrated with reference to specific embodiments, those skilled in the art will recognize that modifications and variations may be made without departing from the principles of the invention as described above and set forth in the following claims. For example, while the invention has been described with reference to a “Sales” table and a “Customer” tables the invention may be used in conjunction with any table from a relational database. Furthermore, the analytical data models of the present invention may comprise any number of dimensions corresponding to any number of data attributes. Accordingly, reference should be made to the appended claims as indicating the scope of the invention.

Claims (27)

    We claim:
  1. 1. A method in combination with first and second tables of data, the first table organizing a first type according to a first attribute, and a second attribute and the second table organizing a second type according to a third attribute, the first attribute of the first table being related to the third attribute of the second table such that the first table may be cross-referenced to the second table thereby, the second attribute of the first table being related to the third attribute of the second table such that the first table may be cross-referenced to the second table thereby, the method comprising:
    modeling a measure according to the first type of the first table;
    modeling a dimension according to the second type of the second table;
    tying the measure to the dimension according to the first attribute of the first table and the third attribute of the second table to allow the measure to be analyzed by the dimension according to the first attribute; and
    tying the measure to the dimension according to the second attribute of the first table and the third attribute of the second table to allow the measure to be analyzed by the dimension according to the second attribute, whereby the dimension provides data according to both the first attribute of the first table and the second attribute of the first table.
  2. 2. The method of claim 1, comprising modeling a measure according to the first type of the first table, the first table comprising data stored in a relational database.
  3. 3. The method of claim 1, comprising modeling a dimension according to the second type of the second table, the second table comprising data stored in a relational database.
  4. 4. The method of claim 1, comprising modeling a dimension according to the second type of the second table, the second table having data organized hierarchically therein.
  5. 5. The method of claim 4, further comprising aggregating the data of the dimension according to the hierarchical organization of the second table.
  6. 6. The method of claim 1, comprising modeling a measure of a data cube according to the first type of the first table.
  7. 7. The method of claim 6, comprising modeling a measure of a data cube formatted for online analytical processing according to the first type of the first table.
  8. 8. The method of claim 1, comprising modeling a dimension of a data cube according to the second type of the second table.
  9. 9. The method of claim 8, comprising modeling a dimension of a data cube formatted for online analytical processing according to the second type of the second table.
  10. 10. A computer readable medium having stored thereon computer readable instructions in combination with first and second tables of data, the first table organizing a first type according to a first attribute, and a second attribute and the second table organizing a second type according to a third attribute, the first attribute of the first table being related to the third attribute of the second table such that the first table may be cross-referenced to the second table thereby, the second attribute of the first table being related to the third attribute of the second table such that the first table may be cross-referenced to the second table thereby, the computer readable instructions for performing the following steps:
    modeling a measure according to the first type of the first table;
    modeling a dimension according to the second type of the second table;
    tying the measure to the dimension according to the first attribute of the first table and the third attribute of the second table to allow the measure to be analyzed by the dimension according to the first attribute; and
    tying the measure to the dimension according to the second attribute of the first table and the third attribute of the second table to allow the measure to be analyzed by the dimension according to the second attribute, whereby the dimension provides data according to both the first attribute of the first table and the second attribute of the first table.
  11. 11. The computer readable medium of claim 10, comprising instructions for performing the step of modeling a measure according to the first type of the first table, the first table comprising data stored in a relational database.
  12. 12. The computer readable medium of claim 10, comprising instructions for performing the step of modeling a dimension according to the second type of the second table, the second table comprising data stored in a relational database.
  13. 13. The computer readable medium of claim 10, comprising instructions for performing the step of modeling a dimension according to the second type of the second table, the second table having data organized hierarchically therein.
  14. 14. The computer readable medium of claim 13, further comprising instructions for performing the step of aggregating the data of the dimension according to the hierarchical organization of the second table.
  15. 15. The computer readable medium of claim 10, comprising instructions for performing the step of modeling a measure of a data cube according to the first type of the first table.
  16. 16. The computer readable medium of claim 15, comprising instructions for performing the step of modeling a measure of a data cube formatted for online analytical processing according to the first type of the first table.
  17. 17. The computer readable medium of claim 10, comprising instructions for performing the step of modeling a dimension of a data cube according to the second type of the second table.
  18. 18. The computer readable medium of claim 17, comprising instructions for performing the step of modeling a dimension of a data cube formatted for online analytical processing according to the second type of the second table.
  19. 19. A system in combination with first and second tables of data, the first table organizing a first type according to a first attribute, and a second attribute and the second table organizing a second type according to a third attribute, the first attribute of the first table being related to the third attribute of the second table such that the first table may be cross-referenced to the second table thereby, the second attribute of the first table being related to the third attribute of the second table such that the first table may be cross-referenced to the second table thereby, the system comprising:
    a processor operative to execute computer executable instructions; and
    memory having stored therein computer executable instructions for performing the following steps:
    modeling a measure according to the first type of the first table;
    modeling a dimension according to the second type of the second table;
    tying the measure to the dimension according to the first attribute of the first table and the third attribute of the second table to allow the measure to be analyzed by the dimension according to the first attribute; and
    tying the measure to the dimension according to the second attribute of the first table and the third attribute of the second table to allow the measure to be analyzed by the dimension according to the second attribute, whereby the dimension provides data according to both the first attribute of the first table and the second attribute of the first table.
  20. 20. The system of claim 19, comprising computer executable instructions for performing the step of modeling a measure according to the first type of the first table, the first table comprising data stored in a relational database.
  21. 21. The system of claim 19, comprising computer executable instructions for performing the step of modeling a dimension according to the second type of the second table, the second table comprising data stored in a relational database.
  22. 22. The system of claim 19, comprising computer executable instructions for performing the step of modeling a dimension according to the second type of the second table, the second table having data organized hierarchically therein.
  23. 23. The system of claim 22, further comprising computer executable instructions for performing the step of aggregating the data of the dimension according to the hierarchical organization of the second table.
  24. 24. The system of claim 19, comprising computer executable instructions for performing the step of modeling a measure of a data cube according to the first type of the first table.
  25. 25. The system of claim 24, comprising computer executable instructions for performing the step of modeling a measure of a data cube formatted for online analytical processing according to the first type of the first table.
  26. 26. The system of claim 19, comprising computer executable instructions for performing the step of modeling a dimension of a data cube according to the second type of the second table.
  27. 27. The system of claim 26, comprising computer executable instructions for performing the step of modeling a dimension of a data cube formatted for online analytical processing according to the second type of the second table.
US10199913 2002-07-19 2002-07-19 System and method for analytically modeling data organized according to related attributes Active 2023-09-26 US7287022B2 (en)

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EP20030016262 EP1383064A3 (en) 2002-07-19 2003-07-17 System and method for analytically modeling data organized according to related attributes

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20100057700A1 (en) * 2008-08-28 2010-03-04 Eric Williamson Systems and methods for hierarchical aggregation of multi-dimensional data sources
US20110161384A1 (en) * 2005-11-02 2011-06-30 Wykes Nathan E System and method for storing item attributes in an electronic catalog
WO2015027831A1 (en) * 2013-08-26 2015-03-05 Tencent Technology (Shenzhen) Company Limited Multidimensional data processing method and device

Families Citing this family (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN100495403C (en) 2007-01-30 2009-06-03 金蝶软件(中国)有限公司 Method and device for processing nonempty date in online analytical processing system
US8150850B2 (en) * 2008-01-07 2012-04-03 Akiban Technologies, Inc. Multiple dimensioned database architecture
US8838524B2 (en) * 2011-08-30 2014-09-16 Gnet Group, Llc Automated system for preparing and presenting control charts
JP5784239B2 (en) * 2012-09-14 2015-09-24 株式会社日立製作所 Data analysis methods, storage medium storing a data analysis device and processing program
US8996544B2 (en) 2012-09-28 2015-03-31 Oracle International Corporation Pruning disk blocks of a clustered table in a relational database management system
US9430550B2 (en) 2012-09-28 2016-08-30 Oracle International Corporation Clustering a table in a relational database management system
US9514187B2 (en) 2012-09-28 2016-12-06 Oracle International Corporation Techniques for using zone map information for post index access pruning

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5978788A (en) * 1997-04-14 1999-11-02 International Business Machines Corporation System and method for generating multi-representations of a data cube
US6014656A (en) * 1996-06-21 2000-01-11 Oracle Corporation Using overlapping partitions of data for query optimization
US6101502A (en) * 1997-09-26 2000-08-08 Ontos, Inc. Object model mapping and runtime engine for employing relational database with object oriented software
US6480836B1 (en) * 1998-03-27 2002-11-12 International Business Machines Corporation System and method for determining and generating candidate views for a database
US6484179B1 (en) * 1999-10-25 2002-11-19 Oracle Corporation Storing multidimensional data in a relational database management system
US6578030B1 (en) * 2000-06-30 2003-06-10 Requisite Technology Inc. Method and apparatus for mapping one catalog into another catalog
US7171427B2 (en) * 2002-04-26 2007-01-30 Oracle International Corporation Methods of navigating a cube that is implemented as a relational object

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2001216307A (en) * 2000-01-31 2001-08-10 Teijin Ltd Relational database management system and storage medium stored with same
JP2002056159A (en) * 2000-08-07 2002-02-20 Union Capital & Holding Ltd Electronic commerce system using communication network

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6014656A (en) * 1996-06-21 2000-01-11 Oracle Corporation Using overlapping partitions of data for query optimization
US5978788A (en) * 1997-04-14 1999-11-02 International Business Machines Corporation System and method for generating multi-representations of a data cube
US6101502A (en) * 1997-09-26 2000-08-08 Ontos, Inc. Object model mapping and runtime engine for employing relational database with object oriented software
US6480836B1 (en) * 1998-03-27 2002-11-12 International Business Machines Corporation System and method for determining and generating candidate views for a database
US6484179B1 (en) * 1999-10-25 2002-11-19 Oracle Corporation Storing multidimensional data in a relational database management system
US6578030B1 (en) * 2000-06-30 2003-06-10 Requisite Technology Inc. Method and apparatus for mapping one catalog into another catalog
US7171427B2 (en) * 2002-04-26 2007-01-30 Oracle International Corporation Methods of navigating a cube that is implemented as a relational object

Cited By (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20110161384A1 (en) * 2005-11-02 2011-06-30 Wykes Nathan E System and method for storing item attributes in an electronic catalog
US8112461B2 (en) * 2005-11-02 2012-02-07 Requisite Software, Inc. System and method for storing item attributes in an electronic catalog
US20100057700A1 (en) * 2008-08-28 2010-03-04 Eric Williamson Systems and methods for hierarchical aggregation of multi-dimensional data sources
US8495007B2 (en) * 2008-08-28 2013-07-23 Red Hat, Inc. Systems and methods for hierarchical aggregation of multi-dimensional data sources
WO2015027831A1 (en) * 2013-08-26 2015-03-05 Tencent Technology (Shenzhen) Company Limited Multidimensional data processing method and device

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